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According to Hull [29], the t test performs well even for distributions which are not perfectly normal.
Considering robustness at the analysis stage, the t test performs well under certain large departures from normality [ 36].
In brief, this test performs a statistic on the T-values of both GM atrophy, denoted here SGM and T-values of hypoperfusion, denoted SPerf.
Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions.
One can see that independent of the pre-processing method, the DFC test performs in general slightly better than CAT diag) and much better than t-test.
Generally, AUC values of 0.5 indicate that a test performs no better than chance, values between 0.70 and 0.79 indicate fair performance, values between 0.80 and 0.89 indicate good performance, and values ≥0.9 indicate excellent test performance.
Monte Carlo simulations indicate that the test performs well in finite samples.
This is the only criterion by which the test performs well.
Torres, who has never failed a drug test, performs an exhaustive dry-land regimen of flexibility and weight training.
Numerical evidence suggests that our test performs well in finite samples.
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Interestingly, when the given q-value is small, a regular t-test performs better than a t-test with a permutation in SAM; this implies permutation-based methods are not appropriate in this situation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com